{"id":"W2954157033","doi":"10.1080/08927022.2019.1632448","title":"Predicting CO<sub>2</sub> adsorption and reactivity on transition metal surfaces using popular density functional theory methods","year":2019,"lang":"en","type":"article","venue":"Molecular Simulation","topic":"CO2 Reduction Techniques and Catalysts","field":"Energy","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; National Supercomputing Centre Singapore; National Research Foundation; Western Canada Research Grid; Nanyang Technological University; National Research Foundation Singapore; Compute Canada","keywords":"Adsorption; Density functional theory; Chemistry; Molecule; Thermodynamics; Binding energy; Work (physics); Phase (matter); Reactivity (psychology); Physical chemistry; Metal; Computational chemistry; Atomic physics; Organic chemistry; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008600858,0.0001896539,0.0002111607,0.0001548893,0.0001295189,0.00004385975,0.00003837441,0.0001740572,0.00003220041],"category_scores_gemma":[0.00006738524,0.0002023254,0.0001197043,0.0001815733,0.00003391976,0.0003222362,0.00001978385,0.0001854967,0.00001129499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001228231,"about_ca_system_score_gemma":0.00001821864,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009984861,"about_ca_topic_score_gemma":0.000007621461,"domain_scores_codex":[0.9983222,0.0005476236,0.0002416494,0.0004128461,0.0003040727,0.0001715648],"domain_scores_gemma":[0.9993394,0.0001090616,0.0001493874,0.0002478312,0.00009783471,0.00005651193],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000145977,0.00003475512,0.0003437808,0.00001479378,0.00003509912,0.00000159582,0.00007915677,0.307136,0.67203,0.001637486,5.818629e-7,0.01854078],"study_design_scores_gemma":[0.0003013037,0.00007376241,0.004858904,0.00002517406,0.00008387594,0.00001454814,0.00004743319,0.2962473,0.6943347,0.003764739,0.00007315771,0.0001751004],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7296535,0.0000498484,0.2696047,0.00001926354,0.0001512483,0.0002265786,0.000004291409,0.0001454589,0.0001451422],"genre_scores_gemma":[0.9980368,0.000006480473,0.001614497,0.00006730964,0.00006575778,0.000007122928,0.0001548634,0.00003341499,0.00001375032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2683833,"threshold_uncertainty_score":0.8250591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0205713507866011,"score_gpt":0.2933212746098626,"score_spread":0.2727499238232615,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}